This is an R Markdown Notebook
Deep Learning… In this lecture I would like to esplore with you the DeepLearning we can do with H2O package in R…
names(DF_NORMAL)
[1] "DateCheck" "Inf1" "Inf2" "Inf3" "Inf4" "Centr1" "Centr2" "Out1" "Out2"
[10] "Out3" "Out4"
about the dataset
summary(NORM_2016)
Inf1 Inf2 Inf3 Inf4 Centr1 Centr2 Out1 Out2
Min. :1060 Min. :1004 Min. :1698 Min. :1698 Min. :2695 Min. :3300 Min. :1360 Min. :1593
1st Qu.:1110 1st Qu.:1360 1st Qu.:1945 1st Qu.:1998 1st Qu.:2802 1st Qu.:3380 1st Qu.:1660 1st Qu.:2012
Median :1130 Median :1427 Median :1998 Median :2043 Median :2841 Median :3430 Median :1708 Median :2068
Mean :1131 Mean :1423 Mean :1993 Mean :2042 Mean :2843 Mean :3429 Mean :1705 Mean :2066
3rd Qu.:1150 3rd Qu.:1489 3rd Qu.:2041 3rd Qu.:2091 3rd Qu.:2886 3rd Qu.:3470 3rd Qu.:1757 3rd Qu.:2121
Max. :1200 Max. :1684 Max. :2202 Max. :2295 Max. :3057 Max. :3660 Max. :2026 Max. :2316
Out3 Out4
Min. :46.51 Min. :66.51
1st Qu.:64.79 1st Qu.:80.00
Median :68.84 Median :82.59
Mean :69.01 Mean :82.60
3rd Qu.:73.33 3rd Qu.:85.37
Max. :86.91 Max. :94.74
This dataset was selected .. after .. the period of deep process maintenance
NORM_2016 <- DF_NORMAL %>% select(2:11) %>% as.matrix()
plot_ly(z = NORM_2016, type = "surface")
TEST_2015 <- DF_TEST %>% select(2:11) %>% as.matrix()
plot_ly(z = TEST_2015, type = "surface")
TEST_2017 <- DF_ANOMALY %>% select(2:11) %>% as.matrix()
plot_ly(z = TEST_2017, type = "surface")
NORM_2016_sc <- DF_NORMAL %>% select(2:11) %>% scale() %>% as.matrix()
normality_model <- h2o.deeplearning(x = names(train),
training_frame = train,
activation = "Tanh",
autoencoder = TRUE,
hidden = c(200,100,200),
sparse = TRUE,
l1 = 1e-4,
epochs = 100)
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normality_model_sc <- h2o.deeplearning(x = names(train_sc),
training_frame = train_sc,
activation = "Tanh",
autoencoder = TRUE,
hidden = c(200,100,200),
sparse = TRUE,
l1 = 1e-4,
epochs = 100)
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h2o.shutdown(prompt = F)
Error in h2o.shutdown(prompt = F) : There is no H2O instance running.